DataScience Online Training

DataScience Online Training

Cheyat Technologies provide best Data Science online training by our highly professional and certified Real time Consultants. Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from detain various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, machine learning, data mining, and predictive analytics, similar to Knowledge Discovery in Databases (KDD). Our Data Science Online Training resolve many queries for the clients by providing real time support. Along With Data Science Online Training, We will provide Placement assistance and On - Job Support also.

DataScience Online Training Course content

Introduction to Analytics

  • What is Analytics?
  • Why is analytics required?
  • Different terms by which Analytics is known in the market

Getting to know your data

  • Data Objective and Attribute typesWhat is an attribute?
  • Nominal Attributes
  • Binary Attributes
  • Ordinal Attributes
  • Numeric Attributes
  • Discrete vs Continuous Attributes
  • Basic Statistical Description of the data
  • Data Visualization

Data Preprocessing

  • Why to preprocess the data?
  • Major tasks in Data Pre-processing
  • Data Cleaning

Introduction to SAS and R

  • Basics of SAS and R
  • Cleaning data in SAS and R
  • Writing Codes in SAS and R
  • Reading outputs in SAS and R

Statistical Method for Decision Making

  • Data Collection, presentation and visualization
  • Hypothesis Testing and Estimation
  • Goodness of fit
  • Test of Independence

Machine Learning

  • What is Supervised Learning and Unsupervised Learning
  • Linear Regression (Simple and Multiple)
  • Logistic Regression
  • Cluster Analysis
  • Factor Analysis
  • Decision Trees
  • Na├»ve Bayes
  • Random Forest
  • CART Technique
  • Principle Component Analysis

Machine Learning with SAS and R

  • Predictive Modelling Concepts
  • How to prepare model
  • Test and Training data
  • Over fitting and Under fitting of the model
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